class_docstr: Data object to store counts of various parameters during training.
  Includes counts for distortion.
class_docstr_tok: ["Data","object","to","store","counts","of","various","parameters","during","training",".","Includes","counts","for","distortion","."]
code: def __init__(self):
      super().__init__()
      self.head_distortion = defaultdict(
          lambda: defaultdict(lambda: defaultdict(float))
      )
      self.head_distortion_for_any_dj = defaultdict(
          lambda: defaultdict(float)
      )
      self.non_head_distortion = defaultdict(lambda: defaultdict(float))
      self.non_head_distortion_for_any_dj = defaultdict(float)
  
file_path: nltk/nltk/translate/ibm4.py
imports: import warnings
  from collections import defaultdict
  from math import factorial
  from nltk.translate import AlignedSent, Alignment, IBMModel, IBMModel3
  from nltk.translate.ibm_model import Counts, longest_target_sentence_length
label_desc: Use image node_id 1 to create a new Model4Counts object from inherited base classes: Counts with example: obj = Model4Counts()